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Data Envelopment Analysis as a Complement to Marginal Analysis

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Author Info
Theodoridis, A.M.
Psychoudakis, A.
Christofi, A.
Abstract

The consideration in the present study is mainly conceptual. The objective is to show how Data Envelopment Analysis (DEA) can be used to reveal the true input-output relations in an industry. In the estimation of a production function it is assumed that all firms use the existing technology efficiently. However, in the real world the observed firms produce homogeneous outputs with differences in factor intensities and in managerial capacity. Hence, inefficiencies are hidden in the estimated production functions. In order to overcome this drawback of the parametric approach and to reveal the true nature of the input-output relations in production, given the available technology, the DEA approach is applied. In this study DEA is applied in order to select the farms that utilize efficiently the existing technology, allowing the estimation of a production function that reveals the true input-output relations in sheep-goat farming, using farm accounting data from a sample of 108 sheep-goat farms.

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Article provided by Greek Association of Agricultural Economists in its journal Agricultural Economics Review.

Volume (Year): 07 (2006)
Issue (Month): 2 (August)
Pages:
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Handle: RePEc:ags:aergaa:44113

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  1. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July. [Downloadable!] (restricted)
  2. Léopold Simar, Paul W. Wilson, 2000. "A general methodology for bootstrapping in non-parametric frontier models," Journal of Applied Statistics, Taylor and Francis Journals, vol. 27(6), pages 779-802, August. [Downloadable!] (restricted)
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  3. Kneip, A & Park, B-U & Simar, L, 1996. "A Note on the Convergence of Nonparametric DEA Efficiency Measures," Papers 9603, Catholique de Louvain - Institut de statistique.
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  4. William H. Greene, 1993. "Frontier Production Functions," Working Papers 93-20, New York University, Leonard N. Stern School of Business, Department of Economics.
  5. Corbo, Vittorio & Meller, Patricio, 1979. "The translog production function : Some evidence from establishment data," Journal of Econometrics, Elsevier, vol. 10(2), pages 193-199, June. [Downloadable!] (restricted)
  6. Simar, L. & Wilson, P.W., 1999. "Statistical Inference in Nonparametric Frontier Models: the State of the Art," Papers 9904, Catholique de Louvain - Institut de statistique.
  7. Luthgren, Mickael & Tambour, Magnus, 1999. "Bootstrapping the Data Envelopment Analysis Malmquist Productivity Index," Applied Economics, Taylor and Francis Journals, vol. 31(4), pages 417-25, April. [Downloadable!] (restricted)
  8. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November. [Downloadable!] (restricted)
  9. Tsybakov, A.B. & Korostelev, A.P. & Simar, L., 1992. "Efficient Estimation of Monotone Boundaries," Papers 9209, Catholique de Louvain - Institut de statistique.
  10. Schmidt, Peter & Sickles, Robin, 1977. "Some Further Evidence on the Use of the Chow Test under Heteroskedasticity," Econometrica, Econometric Society, vol. 45(5), pages 1293-98, July. [Downloadable!] (restricted)
  11. Lau, Lawrence J & Yotopoulos, Pan A, 1971. "A Test for Relative Efficiency and Application to Indian Agriculture," American Economic Review, American Economic Association, vol. 61(1), pages 94-109, March.
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